Solar Dish Field System Model for Spacing Optimization
ASME 2007 Energy Sustainability Conference
Dish Stirling power generation systems have been identified by DOE, Sandia National Laboratories, and Stirling Energy Systems (SES) as having the capability of delivering utility-scale renewable energy to the nation's electrical grid. SES has proposed large plants, 20,000 units or more (0.5 GW rated power) in one place, in order to rapidly ramp up production automation. With the large capital investment needed in such a plant it becomes critical to optimize the system at the field level, as
... as at the individual unit level. In this new software model, we provide a tool that predicts the annual and monthly energy performance of a field of dishes, in particular taking into account the impact of dish-to-dish shading on the energy and revenue streams. The Excel-based model goes beyond prior models in that it incorporates the true dish shape (flexible to accommodate many dish designs), multiple-row shading, and a revenue stream model that incorporates time-of-day and time-of-year pricing. This last feature is critical to understanding key shading tradeoffs on a financial basis. The model uses TMY or 15-minute meteorological data for the selected location. It can incorporate local ground slope across the plant, as well as stagger between the rows of dish systems. It also incorporates field-edge effects, which can be significant on smaller plants. It also incorporates factors for measured degraded performance due to shading. This tool provides one aspect of the decision process for fielding many systems, and must be combined with land costs, copper layout and costs, and O&M predictions (driving distance issues) in order to optimize the loss of power due to shading against the added expense of a larger spatial array. Considering only the energy and revenue stream, the model indicates that a rectangular, unstaggered field layout maximizes field performance. We also found that recognizing and accounting for true performance degradation due to shading significantly impacts plant production, compared with prior modeling attempts.